A vector library is a software tool or framework that provides functionalities for managing and searching high-dimensional vectors. These libraries are commonly used in applications requiring similarity search, such as recommendation systems, image retrieval, and natural language processing.
A vector library enables operations like vector storage, indexing, and querying. It supports distance metrics like cosine similarity or Euclidean distance to measure how closely vectors resemble each other. Popular libraries include Faiss (Facebook AI Similarity Search), Annoy, and HNSWlib, each optimized for specific use cases and scalability requirements.
Vector libraries often implement Approximate Nearest Neighbor (ANN) algorithms for fast and efficient searches in large datasets. For example, HNSWlib uses Hierarchical Navigable Small World graphs for low-latency retrieval.
Developers use vector libraries to build applications like search engines, where embeddings from machine learning models represent text, images, or audio. These embeddings are stored and queried using the library to find semantically similar items.
When choosing a vector library, factors like scalability, indexing techniques, and compatibility with AI frameworks should be considered. These libraries play a crucial role in bridging the gap between AI models and production-ready systems.